FogDLearner: A Deep Learning-based Cardiac Health Diagnosis Framework using Fog Computing
Open accessAbstract
The application of the Internet of Things (IoT) and Artificial Intelligence (AI) in healthcare is an emerging domain. In Healthcare applications, relying on both IoT and AI requires paying attention to latency, responsiveness and management of data loads. Most of the healthcare applications are based on Cloud computing and use Cloud platforms such as Google Cloud and Microsoft Azure. With the increased adoption of IoT in various domains, the data generation rate and volume by IoT devices has tremendously increased, making the Cloud insufficient for latency sensitive healthcare applications. Fog computing, complementing the Cloud services, can be deployed close to the data source to better utilize distributed resources and meet the Quality of Service (QoS) requirements of healthcare application. In this paper, we propose a Fog-based cardiac health detection framework, called FogDLearner. FogDLearner utilizes distributed resources to diagnose cardiac health of a person without compromising QoS and accuracy. FogDLearner uses a deep learning based classifier to predict the cardiac health of the user. The performance of the proposed framework is evaluated on the PureEdgeSim simulator, in terms of resource utilization under overload and under-load scenarios, mobility support, and power consumption. The experimental results show the validity of proposed work for support of mobile applications.
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Edge AI has a wide range of applications across various industries.
“Edge AI has a wide range of applications in various industries [54].”
From Edge AI: A survey (2023) · cites this paper as [54]
The Abstract describes an AI-based cardiac diagnosis system using fog computing, which supports healthcare as one application. Its reference to IoT adoption in various domains does not establish that Edge AI has applications across various industries; the Introduction likewise focuses on healthcare. The indexed passages provide no page numbers. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIf Edge Computing processes data at the edge network, context and location awareness are much more readily obtainable and achievable.
“If Edge Computing processes data at the edge network, then context and location awareness are much more readily obtainable and achievable [54].”
From Edge AI: A survey (2023) · cites this paper as [54]
The Introduction says fog nodes process data near its source, and Section 5.2 reports benefits for latency and network usage. Neither passage says that processing at the edge makes context or location awareness easier to obtain; searches for those terms found no such finding in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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- Authors
- Iftikhar, S., Golec, M., Chowdhury, D., Gill, S. S., & Uhlig, S.
- Affiliations
- Queen Mary University of London (GB)
- International Institute of Information Technology (IN)
- Venue
- Australasian Computer Science Week 2022
- Publisher
- ACM
- Published
- 2022-02-14
- Pages
- 136-144
- OpenAlex
- W4220818748
- Cited by
- 31
- References
- 16
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 10 · 2023: 2 · 2022: 8
- FWCI
- 6.54
- Type
- conference paper
- Language
- en
- Open access
- green
- qmro.qmul.ac.uk
- Locations
- Australasian Computer Science Week Multiconference (ACSW) (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- Higher Education Commission, Pakistan (2-5/FDPOS/HRD/UoK/QMUL/2020/1)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- fog computing, deep learning classifier, Internet of Medical Things, edge computing, quality of service, delay-sensitive applications, resource utilization, mobile health apps, power consumption
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